Python ARIMA 模型，预测值发生偏移 [英] Python ARIMA model, predicted values are shifted

问题描述

我是 Python ARIMA 实现的新手.我有几个月的数据，频率为 15 分钟.在我尝试遵循 Box-Jenkins 方法来拟合时间序列模型时.我在最后遇到了一个问题.时间序列 (ts) 和差异序列 (ts_diff) 的 ACF-PACF 图给出.我使用了 ARIMA (5,1,2)，最后我绘制了拟合值(绿色)和原始值(蓝色).正如您可以从 figure 中看到的那样，值有明显的变化(一个).我做错了什么?

I am new to Python ARIMA implementation. I have a data at 15 min frequency for few months. In my attempt to follow the Box-Jenkins method to fit a timeseries model. I ran into an issue towards the end. The ACF-PACF graph for the time series (ts) and the difference series (ts_diff) are given. I used ARIMA (5,1,2) and finally I plotted the fitted values(green) and original values(blue). As you can from figure, there is a clear shift(by one) in values. What am I doing wrong?

预测不好吗?任何见解都会有所帮助.

Is the prediction bad? Any insight will be helpful.

推荐答案

这是提前预测或预测的标准属性.

This is a standard property of one-step ahead prediction or forecasting.

用于预测的信息是截至并包括上一期的历史记录.例如，某个时段的高峰会影响下一个时段的预测，但不会影响高峰时段的预测.这使得预测在图中出现偏移.

The information used for the forecast is the history up to and including the previous period. A peak, for example, at a period will affect the forecast for the next period, but cannot influence the forecast for the peak period. This makes the forecasts appear shifted in the plot.

提前两步的预测会给人留下两个时期变化的印象.

A two-step ahead forecast would give the impression of a shift by two periods.

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